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gistfile1.txt
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"name": "",
"signature": "sha256:dcb810a9650e942e8d68b058fe09b2314ef7520b461f67c444bcfcab490b6cb0"
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import scipy.stats as ss\n",
"import os\n",
"\n",
"n_cpu = 8\n",
"\n",
"os.environ['MKL_NUM_THREADS'] = str(n_cpu+1)\n",
"os.environ['OMP_NUM_THREADS'] = str(n_cpu+1)\n",
"os.environ['OMP_STACKSIZE'] = \"16M\"\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import cPickle as pickle\n",
"\n",
"import pywt\n",
"\n",
"import pymouse\n",
"from pymouse.scripts.consistency import *\n",
"from sklearn.decomposition import RandomizedPCA\n",
"from sklearn.preprocessing import scale\n",
"\n",
"import pyhsmm\n",
"from pyhsmm.basic.distributions import NegativeBinomialIntegerR2Duration\n",
"from pyhsmm.util.text import progprint_xrange\n",
"import autoregressive.models as models\n",
"import autoregressive.distributions as distributions\n",
"import sklearn.preprocessing as prep\n",
"import pandas as pd\n",
"\n",
"\n",
"def _get_hol(model, d):\n",
" model.add_data(data=d, generate=False)\n",
" return model.states_list.pop().log_likelihood()\n",
"\n",
"def get_hol(model, data_test, n_jobs=1):\n",
" from joblib import Parallel, delayed\n",
" n_pts = float(len(data_test))\n",
" split_data_test = np.array_split(data_test, n_jobs)\n",
" likes = Parallel(n_jobs=n_jobs, pre_dispatch=n_jobs)(delayed(_get_hol)(model,d) for d in split_data_test)\n",
" heldout_likelihood = np.sum(likes)/n_pts\n",
" return heldout_likelihood\n",
"\n",
"def from_gaussian_approx(meanmean,meanvar,varmean,varvar,r_max,n_rej_iter=30,tol=1e-4,Nsamples=1e6):\n",
" import matplotlib.pyplot as plt\n",
" prop_meansamples = np.random.normal(meanmean,np.sqrt(meanvar),size=Nsamples)\n",
" prop_varsamples = np.random.normal(varmean,np.sqrt(varvar),size=Nsamples)\n",
" \n",
" r_samples, p_samples = [], []\n",
" for i in range(n_rej_iter):\n",
" good = (prop_varsamples > tol) & (prop_meansamples > tol) & (prop_varsamples > prop_meansamples + tol)\n",
" prop_meansamples, prop_varsamples = prop_meansamples[good], prop_varsamples[good]\n",
" prop_r_samples, prop_p_samples = meanvar_to_rp(prop_meansamples,prop_varsamples)\n",
" good = prop_r_samples <= r_max\n",
" r_samples.extend(prop_r_samples[good])\n",
" p_samples.extend(prop_p_samples[good])\n",
"\n",
" if len(r_samples) > Nsamples/2:\n",
" break\n",
" \n",
" assert len(r_samples) > Nsamples/2\n",
" r_samples = np.hstack(r_samples)\n",
" p_samples = np.hstack(p_samples)\n",
" \n",
" r_discrete_distn = np.bincount(r_samples.astype('int64'))[1:].astype('float64')\n",
" r_discrete_distn /= r_discrete_distn.sum()\n",
" alphas_0, betas_0 = map(np.array,zip(*[fit_beta(p_samples[r_samples == r])\n",
" for r in range(1,1+r_discrete_distn.shape[0])]))\n",
" assert np.all(alphas_0 > 0) and np.all(betas_0 > 0)\n",
" \n",
" return {\n",
" 'r_discrete_distn':r_discrete_distn,\n",
" 'alphas_0':alphas_0,\n",
" 'betas_0':betas_0,\n",
" }\n",
"\n",
"def fit_beta(ps):\n",
" 'method-of-moments fit'\n",
" if len(ps) > 1:\n",
" return ss.beta.fit(ps,floc=0,fscale=1)[:2]\n",
" else:\n",
" return (1.,1.)\n",
" \n",
"### converting between (mean,var) and (r,p)\n",
"def meanvar_to_rp(mean,var):\n",
" assert np.all(var > mean), 'must be overdispersed'\n",
" p = 1-mean/var\n",
" r = np.maximum(1,np.round(mean*(1-p)/p))\n",
" return r,p\n",
" \n",
"def rp_to_meanvar(r,p):\n",
" mean = r*p/(1-p)\n",
" var = r*p/(1-p)**2\n",
" return mean, var"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
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{
"cell_type": "code",
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"input": [
"# Get data\n",
"\n",
"store_name = \"/data/behavior/OFA/Datta_C57.h5\"\n",
"train_experiments = ['C57-1_OFA_4-14-14',\n",
" 'C57-9_OFA_4-14-14',\n",
" 'C57-7_OFA_4-14-14',\n",
" 'C57-4_OFA_4-14-14',\n",
" 'C57-6_OFA_4-14-14',\n",
" 'C57-3_OFA_4-14-14',\n",
" 'C57-5_OFA_4-14-14',\n",
" 'C57-10_OFA_4-14-14']\n",
"test_experiments = ['C57-2_OFA_4-14-14', 'C57-8_OFA_4-14-14']\n",
"\n",
"train_df, arrays = get_behavior(store_name=store_name,\n",
" experiments=train_experiments,\n",
" query=\"index<%d\"%40000,\n",
" array_names=[\"images_in_mm\"])\n",
"train_images = arrays['images_in_mm']\n",
"\n",
"test_df, arrays = get_behavior(store_name=store_name,\n",
" experiments=test_experiments,\n",
" query=\"index<%d\"%40000,\n",
" array_names=[\"images_in_mm\"])\n",
"test_images = arrays['images_in_mm']\n",
"\n",
"# Get features\n",
"\n",
"ndim = D = 10\n",
"train_data, pca = make_pca_data(train_images, ndim)\n",
"test_data, pca = make_pca_data(test_images, ndim, pca=pca)\n",
"train_datas = split_data(train_data, train_df)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
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{
"cell_type": "code",
"collapsed": false,
"input": [
"# Train a model\n",
"\n",
"def go(seed=0):\n",
" np.random.seed(seed)\n",
" Nstates = 40\n",
" nlags = 3\n",
" alpha = 30.\n",
" gamma = 30.\n",
" meanmean = 30\n",
" meanvar = 30\n",
" varmean = 1000\n",
" varvar = 1\n",
" init_state_concentration = 10.\n",
" affine = True\n",
" nu_0 = D+5\n",
" S_0_scale = 1.0\n",
" K_0_scale = 1.0\n",
"\n",
" # Nstates: (5, 80)\n",
" # nlags: (1, 5)\n",
" # alpha: (1, 1e4)\n",
" # gamma: (1e, 1e4)\n",
" # meanmean: (3, 30)\n",
" # meanvar: (1, 1e6)\n",
" # varmean: (1, 1000)\n",
" # varvar: (1, 1e6)\n",
" # init_state_concentration: (1, 1e4)\n",
" # nu_0: (D+1, D+1e6)\n",
" # S_0_scale: (1e-2, 1e2)\n",
" # K_0_scale: (1e-2, 1e2)\n",
"\n",
" dur_hypers = from_gaussian_approx(meanmean=meanmean,\n",
" meanvar=meanvar,\n",
" varmean=varmean,\n",
" varvar=varvar,\n",
" r_max=10)\n",
"\n",
" # NOTE NOTE NOTE SETTING IT TO SOMETHING THAT I LIKE\n",
" dur_hypers = dict(r_discrete_distn=[1.0], alpha_0=1000., beta_0=30.)\n",
"\n",
" obs_distns=[distributions.AutoRegression(\n",
" nu_0=nu_0, S_0=S_0_scale*np.eye(D), M_0=np.zeros((D,D*nlags+affine)),\n",
" K_0=K_0_scale*np.eye(D*nlags+affine), affine=affine) for state in range(Nstates)]\n",
"\n",
" dur_distns=[NegativeBinomialIntegerR2Duration(**dur_hypers) for state in range(Nstates)]\n",
"\n",
" model = models.ARWeakLimitHDPHSMMIntNegBinPython(\n",
" alpha=alpha,gamma=gamma,\n",
" init_state_concentration=init_state_concentration,\n",
" obs_distns=obs_distns,\n",
" dur_distns=dur_distns,\n",
" )\n",
"\n",
" for d in train_datas:\n",
" model.add_data(d)\n",
"\n",
" samples = []\n",
" scores = []\n",
" heldout_likes = []\n",
"\n",
" for itr in progprint_xrange(100):\n",
" model.resample_model()\n",
" scores.append(model.log_likelihood())\n",
" samples.append(np.concatenate(model.stateseqs))\n",
"\n",
" sample_labels = np.vstack(samples)\n",
" labels = sample_labels[-1]\n",
" heldout_likelihood = get_hol(model, test_data, n_jobs=16)\n",
"\n",
" return heldout_likelihood, sample_labels, scores"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"outs = [go(i) for i in range(10)]\n",
"heldout_likes, sample_labels, scores = zip(*outs)"
],
"language": "python",
"metadata": {},
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"/home/dattalab/Code/pyhsmm/basic/pybasicbayes/distributions.py:1845: RuntimeWarning: underflow encountered in multiply\n",
" self._alpha_mf = self.weights * self.alphav_0.sum()\n",
"/home/dattalab/Code/pyhsmm/internals/transitions.py:186: RuntimeWarning: underflow encountered in true_divide\n",
" out /= out.sum(1)[:,na]\n",
"/home/dattalab/Code/pyhsmm/internals/hsmm_inb_states.py:132: RuntimeWarning: underflow encountered in double_scalars\n",
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"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
". [ 100/100, 6.02sec avg, 0.00sec ETA ]\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" 6.02sec avg, 602.35sec total\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"[plot(np.array(s[10:])/len(train_data)) for s in scores]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"[[<matplotlib.lines.Line2D at 0x7fdc4bfe1ad0>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bfe1d50>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bfee450>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bfeea90>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bff9110>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bff9750>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bff9d90>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4c026a90>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bf82a50>],\n",
" [<matplotlib.lines.Line2D at 0x7fdc4bf8f0d0>]]"
]
},
{
"metadata": {},
"output_type": "display_data",
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i3tnzWHj5QmZOn4lbuckN5cj0Z7A5bTgrnDjLnSinItufJbotSnhbmGxbFrPP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\nxO93/Z4nO55ky9AWuiJdxDI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s7J3YbKdgfgmtxeWyc6dEtfz852Khf/GLkjhxSmNoLSze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\nhG+MMWeI/w9ZsTxZL13QTwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fdc4c0b9250>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(heldout_likes, 'o')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"[<matplotlib.lines.Line2D at 0x7fdc4c0a1e10>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fdc62d5f850>"
]
}
]
}
],
"metadata": {}
}
]
}
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